A medical research company is working on image data, where they want to classify microscopic images into cancerous and non-cancerous categories. The boundary between these categories is not linear. Which algorithm would be a strong candidate for this problem?
- Convolutional Neural Network (CNN)
- Logistic Regression
- Naive Bayes Classifier
- Principal Component Analysis
Convolutional Neural Networks (CNNs) are excellent for image classification tasks, especially when dealing with non-linear boundaries. They use convolutional layers to extract features from images, making them suitable for tasks like cancerous/non-cancerous image classification.
The term "exploitation" in reinforcement learning refers to which of the following?
- Utilizing the best-known actions
- Trying new, unexplored actions
- Maximizing exploration
- Modifying the environment
Exploitation involves utilizing the best-known actions to maximize rewards based on current knowledge, minimizing risk and uncertainty.
________ learning is often used for discovering hidden patterns in data.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
Unsupervised learning is a machine learning approach where algorithms are used to identify patterns in data without explicit guidance. It is commonly employed for data exploration and pattern discovery.
When dealing with high-dimensional data, which of the two algorithms (k-NN or Naive Bayes) is likely to be more efficient in terms of computational time?
- Both Equally Efficient
- Naive Bayes
- Neither is Efficient
- k-NN
Naive Bayes is typically more efficient in high-dimensional data due to its simple probabilistic calculations, while k-NN can suffer from the "curse of dimensionality."
In the k-NN algorithm, as the value of k increases, the decision boundary becomes __________.
- Linear
- More complex
- More simplified
- Non-existent
As the value of k in k-NN increases, the decision boundary becomes more simplified because it is based on fewer neighboring data points.
A company wants to segment its customers based on their purchasing behavior. They have a fair idea that there are around 5 distinct segments but want to confirm this. Which clustering algorithm might they start with?
- K-Means Clustering
- Agglomerative Hierarchical Clustering
- Mean-Shift Clustering
- Spectral Clustering
The company might start with K-Means Clustering to confirm their idea of five distinct segments. K-Means is often used for partitioning data into a pre-specified number of clusters and can be a good choice when you have a rough idea of the number of clusters.
Variational autoencoders (VAEs) introduce a probabilistic spin to autoencoders by associating a ________ with the encoded representations.
- Probability Distribution
- Singular Value Decomposition
- Principal Component
- Regression Function
VAEs introduce a probabilistic element to autoencoders by associating a probability distribution (typically Gaussian) with the encoded representations. This allows for generating new data points.
Which regression technique is primarily used for predicting a continuous outcome variable (like house price)?
- Decision Tree Regression
- Linear Regression
- Logistic Regression
- Polynomial Regression
Linear Regression is the most common technique for predicting a continuous outcome variable, such as house prices. It establishes a linear relationship between input features and the output.
In CNNs, the ________ layer is responsible for detecting features in an image.
- Convolutional
- Pooling
- Fully Connected
- Input
The 'Convolutional' layer in a Convolutional Neural Network (CNN) is responsible for detecting features in an image using convolution operations.
Imagine you're developing a model to recognize rare bird species from images. You don't have many labeled examples of these rare birds, but you have a model trained on thousands of common bird species. How might you leverage this existing model for your task?
- Fine-tuning the Pre-trained Model
- Random Initialization of Weights
- Training the Model from Scratch
- Using the Model Only for Common Bird Recognition
Fine-tuning involves taking a pre-trained model and adjusting its parameters, typically only in the final layers, to specialize it for your specific task, which is recognizing rare bird species in this case.